---
title: October 2022
description: Read about DataRobot's new public preview and generally available features released in October, 2022.

---

## October 2022 {: #october-2022 }

_October 26, 2022_

DataRobot's managed AI Platform deployment for October delivered the following new GA and Public Preview features. See the [this month's release announcements](release/index) as well as a [deployment history](cloud-history/index) for additional past feature announcements. See also:

* [**Deprecation notices**](#deprecation-announcements)

??? abstract "Features grouped by capability"
    Name       |  GA | Public Preview
    ---------- | ---- | ---
    **Data and integrations**      |  :~~:  | :~~:
     [Added support for manual transforms of text features](#added-support-for-manual-transforms-of-text-features) |✔ |  |
    **Modeling**      |  :~~:  | :~~:
     [Add custom logos to No-Code AI Apps](#add-custom-logos-to-no-code-ai-apps) | | ✔ |
    **Predictions and MLOps**      |  :~~:  | :~~:
     [Deployment Usage tab](#deployment-usage-tab) | | ✔ |
     [Drill down on the Data Drift tab](#drill-down-on-the-data-drift-tab) | | ✔ |
     [Model logs for model packages](#model-logs-for-model-packages) | | ✔ |


### GA {: #ga }

#### Added support for manual transforms of text features {: #added-support-for-manual-transforms-of-text-features }

With this release, DataRobot now allows manual, user-created variable type transformations from categorical-to-text even when a feature is flagged as having "too many values". These transformed variables will not be included in the Informative Features list, but can be manually added to a feature list for modeling.

### Public Preview {: #public-preview }

#### Deployment Usage tab {: #deployment-usage-tab }

After deploying a model and making predictions in production, monitoring model quality and performance over time is critical to ensure the model remains effective. This monitoring occurs on the [Data Drift](data-drift) and [Accuracy](deploy-accuracy) tabs and requires processing large amounts of prediction data. Prediction data processing can be subject to delays or rate limiting.

On the left side of the **Usage** tab is the **Prediction Tracking** chart, a bar chart of the prediction processing status over the last 24 hours or 7 days, tracking the number of processed, missing association ID, and rate-limited prediction rows. Depending on the selected view (24-hour or 7-day), the histogram's bins are hour-by-hour or day-by-day:

![](images/rn-prediction-usage-tracking.png)

To view additional information on the **Prediction Tracking** chart, hover over a column to see the time range during which the predictions data was received and the number of rows that were **Processed**, **Rate Limited**, or **Missing Association ID**:

![](images/prediction-tracking-details.png)

On the right side of the **Usage** tab are the processing delays for **Predictions Processing (Champion)** and **Actuals Processing** (the delay in actuals processing is for _ALL_ models in the deployment):

![](images/predictions-processing-delay.png)

The **Usage** tab recalculates the processing delays without reloading the page. You can check the **Updated** value to determine when the delays were last updated.

**Required feature flag:** Enable Deployment Processing Info

Public preview [documentation](deploy-usage).

#### Drill down on the Data Drift tab {: #drill-down-on-the-data-drift-tab }

The **Data Drift** > **Drill Down** chart visualizes the difference in distribution over time between the training dataset of the deployed model and the datasets used to generate predictions in production. The drift away from the baseline established with the training dataset is measured using the Population Stability Index (PSI). As a model continues to make predictions on new data, the change in the drift status over time is visualized as a heat map for each tracked feature. This heat map can help you identify [data drift](glossary/index#data-drift) and compare drift across features in a deployment to identify correlated drift trends:

![](images/rn-drill-down-heat-map.png)

In addition, you can select one or more features from the heat map to view a **Feature Drift Comparison** chart, comparing the change in a feature's data distribution between a reference time period and a comparison time period to visualize drift. This information helps you identify the cause of data drift in your deployed model, including data quality issues, changes in feature composition, or changes in the context of the target variable:

![](images/rn-drill-down-drift-compare.png)

**Required feature flag:** Enable Drift Drill Down Plot

Public preview [documentation](data-drift#drill-down-on-the-data-drift-tab).

#### Model logs for model packages {: #model-logs-for-model-packages }

A model package's model logs display information about the operations of the underlying model. This information can help you identify and fix errors. For example, compliance documentation requires DataRobot to execute many jobs, some of which run sequentially and some in parallel. These jobs may fail, and reading the logs can help you identify the cause of the failure (e.g., the Feature Effects job fails because a model does not handle null values).

!!! important
    In the Model Registry, a model package's **Model Logs** tab _only_ reports the operations of the underlying model, not the model package operations (e.g., model package deployment time).

In the **Model Registry**, access a model package, and then click the **Model Logs** tab:

![](images/pp-model-pkg-logs.png)

| | Information | Description |
|-|-------------|-------------|
| ![](images/icon-1.png) | Date / Time | The date and time the model log event was recorded. |
| ![](images/icon-2.png) | Status      | The status the log entry reports: <ul><li><span style="color:#3BC169">INFO</span>: Reports a successful operation.</li><li><span style="color:#E74D4D">ERROR</span>: Reports an unsuccessful operation.</li></ul> |
| ![](images/icon-3.png) | Message     | The description of the successful operation (INFO), or the reason for the failed operation (ERROR). This information can help you troubleshoot the root cause of the error.  |

If you can't locate the log entry for the error you need to fix, it may be an older log entry not shown in the current view. Click **Load older logs** to expand the **Model Logs** view.

![](images/pp-model-pkg-logs-load.png)

!!! tip
    Look for the older log entries at the top of the **Model Logs**; they are added to the top of the existing log history.

**Required feature flag:** Enable Model Logs for Model Packages

Public preview [documentation](pp-model-pkg-logs).

#### Add custom logos to No-Code AI Apps {: #add-custom-logos-to-no-code-ai-apps }

Now available for public preview, you can add a custom logo to your No-Code AI Apps, allowing you to keep the branding of the AI App consistent with that of your company before sharing it either externally or internally.

![](images/app-logo-5.png)

To upload a new logo, open the application you want to edit and click **Build**. Under **Settings > Configuration Settings**, click **Browse** and select a new image, or drag-and-drop an image into the **New logo** field.

![](images/app-logo-3.png)

**Required feature flag:** Enable Application Builder Custom Logos

### Deprecation announcements {: #deprecation-announcements }

#### DataRobot Pipelines to be removed in November {: #datarobot-pipelines-to-be-removed-in-november }

As of November 2022, DataRobot will be retiring Pipeline Workspaces and will no longer continue to support it. If you are currently using this offering, contact [support@datarobot.com](mailto:Support@datarobot.com) or visit [support.datarobot.com](http://support.datarobot.com/).

#### User/Open source models disabled in November {: #user-open-source-models-disabled-in-november }

As of November 2022, DataRobot disabled all models containing User/Open source (“user”) tasks. See the [release announcement](june2022-announce#user-open-source-models-deprecated-and-soon-disabled) for full information on identifying these models. Use the [Composable ML](cml/index) functionality to create custom models.

#### DataRobot Prime models to be deprecated {: #datarobot-prime-models-to-be-deprecated }

[DataRobot Prime](prime/index), a method for creating a downloadable, derived model for use outside of the DataRobot application, will be removed in an upcoming release. It is being replaced with the new ability to export Python or Java code from Rulefit models using the [Scoring Code](scoring-code/index) capabilities. Rulefit models differ from Prime only in that they use raw data for their prediction target rather than predictions from a parent model. There is no change in the availability of Java Scoring Code for other blueprint types, and any existing Prime models will continue to function.

#### Automodel functionality to be removed {: #automodel-functionality-to-be-removed }

The October release deployment brings the removal of the "Automodel" Public Preview functionality. There is no impact to existing projects, but the feature will no longer be accessible from the product.

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